Applications of Artificial Intelligence in Urological Practises: Systematic Review and Future Directions

Abstract

Urological diseases such as prostate cancer, bladder cancer, kidney cancer, benign
prostatic hyperplasia (BPH), and kidney stones represent a significant global health
burden. Over the past decade, data-driven approaches, particularly Artificial Intelligence
(AI), have emerged as transformative tools in urology for clinical practice, diagnosis,
preoperative planning, and patient management. In this systematic review, we examined
published research on the applications of AI in urology and compared findings across
diagnostic, prognostic, and interventional domains. Machine Learning (ML), Deep
Learning (DL), and Natural Language Processing (NLP) have enabled predictive modelling
for prostate, bladder, and kidney cancer, facilitating early detection, risk stratification,
and outcome prediction. AI-driven image analysis has enhanced the interpretation of
ultrasound, CT, and MRI data, while robotic-assisted systems have improved surgical
precision, reduced operating times, and provided intraoperative guidance. Beyond the
operating room, personalized medicine and long-term patient monitoring have been
supported by AI-based decision support systems, Chatbot, and wearable technologies.
Despite these advancements, challenges remain in terms of data heterogeneity,
algorithm transparency, clinical integration, and ethical considerations, particularly
regarding privacy and bias. Our review highlights both the strengths and limitations of
current AI applications in urology and underscores the future trajectory of AI-driven
precision medicine to optimize outcomes and transform patient care.